Group Project
T11_Wed_reprex
1 Data Description
The data we have chosen for this project is the Australian Road Deaths Database (ARDD) provided by the Australian Bureau of Infrastructure and Transport Research Economics (BITRE). The records are updated monthly from 1986 to 2021. Here is the link to the database : https://data.gov.au/dataset/ds-dga-5b530fb8-526e-4fbf-b0f6-aa24e84e4277/details.
There are two data files. The fatality data contains the demographic and details of people who have died within 30 days of the traffic accident due to injuries caused by an Australia road crash. The fatal crashes data contains the records for the crash, including information like the road type, and speed limit. The two data files can be connected by the crash ID. See Appendix for more detailed descriptions of each variable.
2 Research Questions:
Q1: What demographic has a higher rate of traffic accidents?
Q2: Which road user is more prone to accident with respect to States and over the Years?
Q3: How does the car accident fatality link with the district and accident type?
Q4: Is there any correlation between accidents and holiday? And why are some explanations for the relation(s) or lack thereof.
Q5 :Has accident rate decreased or increased within the past decade (2010- 2020)?
Q6: Possible correlations between number of fatalities and speed limit zone
## Crash.ID State Month Year Crash.Type Number.Fatalities Bus.Involvement
## 1 20202001 Vic 12 2020 Multiple 1 No
## 2 20204057 SA 12 2020 Single 1 No
## 3 20203145 Qld 12 2020 Single 1 No
## 4 20203208 Qld 12 2020 Single 1 No
## 5 20203117 Qld 12 2020 Multiple 2 No
## 6 20201199 NSW 12 2020 Single 1 No
## Heavy.Rigid.Truck.Involvement Articulated.Truck.Involvement Speed.Limit
## 1 No Yes 100
## 2 No No 100
## 3 No No 60
## 4 No No 100
## 5 No Yes 100
## 6 No No 50
## National.LGA.Name.2017 Christmas.Period Easter.Period Time.of.day
## 1 Horsham (RC) Yes No Night
## 2 Tatiara (DC) No No Day
## 3 Sunshine Coast (R) No No Night
## 4 Bundaberg (R) Yes No Night
## 5 Cloncurry (S) Yes No Day
## 6 Upper Lachlan No No Day## Crash.ID State Month Year Crash.Type Bus.Involvement
## 1 20203116 Qld 12 2020 Multiple No
## 2 20201210 NSW 12 2020 Single No
## 3 20203202 Qld 12 2020 Single No
## 4 20201093 NSW 12 2020 Single No
## 5 20203161 Qld 12 2020 Multiple No
## 6 20203179 Qld 12 2020 Multiple No
## Heavy.Rigid.Truck.Involvement Articulated.Truck.Involvement Speed.Limit
## 1 No No 60
## 2 No No 100
## 3 No No 100
## 4 No No 100
## 5 No No 70
## 6 No No 100
## Road.User Gender Age National.LGA.Name.2017 Christmas.Period
## 1 Motorcycle rider Male 28 Cairns (R) No
## 2 Passenger Male 89 Walcha Yes
## 3 Driver Male 21 Charters Towers (R) Yes
## 4 Driver Male 81 Walcha No
## 5 Driver Female 66 Moreton Bay (R) No
## 6 Passenger Male 79 Lockyer Valley (R) No
## Easter.Period Age.Group Time.of.day
## 1 No 26_to_39 Night
## 2 No 75_or_older Day
## 3 No 17_to_25 Day
## 4 No 75_or_older Night
## 5 No 65_to_74 Night
## 6 No 75_or_older Day3 Data Analysis
3.1 Q. What demographic has a higher rate of road accidents or fatal crashes?
- sex as a factor in accident
- Age as a factor in accident
Figure 3.1: Australian road deaths database and fatal crashes, by age and gender
3.1.1 Analysis
- The Australian Road Deaths Database provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities.
- Road deaths from recent months are preliminary and the series is subject to revision.
- The above graph 3.1 explains, what demographic has a higher rate of traffic accidents considering Age.Group and Gender as a factor.
- The age group between 40 to 64 in both the genders, had the maximum risk of fatal crash involvements compared to the rest of the age groups.
- Male drivers compared to Female for the same age group have the highest rate of road accidents i.e. more than 3000 fatal crashes. Hence, Male drivers between age group 40 t0 64 are more susceptible to the fatal crash involvement.
3.2 Q. Which road user is more prone to accident with the States and over the Year?
With respect to Year
Figure 3.2: Australian road deaths database and fatal crashes, by age and gender
3.2.1 Analysis
- This analysis is with respect to the vulnerable Road Users who are prone to road accidents over the years in Australia.
- According the above graph 3.2, Driver is one of the most vulnerable road user prone to accident through-out the years.
- In 2010 and 2016 driver experienced the peak where there were maximum number of casualties.
- Along with that even Pedestrian, Passenger and Motorcycle rider are among the second most vulnerable road user, prone to accident.
- The number of casualties for Motorcycle rider and Pedestrian has mostly been constant through-out the years from 2010 till 2020
- The casualties for Passenger shows a slight decrease over the years.
we group the data by states and the type of Road Users which have involved in the fatal accidents.
Figure 3.3: Number of Fatal Accidents with Different Road User in Different State
3.2.2 Analysis
From the graph above 3.3:
In all the states
- more than half of the total fatal accidents with drivers involved.
- Accidents with Passenger involved account for the second large portion of the total accidents
- follow by motorcycle rider and then pedestrian involved accidents.
Interpretation:
- Most fatal accidents with the drivers and passengers involved. relatively less pedestrian involved in fatal car accidents. We may interpret that most of the people drive in Australia, that is the reason more drivers and passenger involved in fatal car accidents. Another interpretation is most of the car accidents happened in highway or it is a crashed between cars or crashed between car and the road.
3.3 Q. How does the car accident fatality link with the district and accident type?
First of all, we group the data by state and the type of car which have involved in the fatal accidents.
## # A tibble: 6 x 2
## # Groups: State [6]
## State Bus_Involved
## <chr> <int>
## 1 ACT 4
## 2 NSW 75
## 3 NT 6
## 4 Qld 43
## 5 SA 13
## 6 Tas 7## # A tibble: 6 x 2
## # Groups: State [6]
## State Heavytruck_Involved
## <chr> <int>
## 1 ACT 3
## 2 NSW 266
## 3 NT 5
## 4 Qld 144
## 5 SA 53
## 6 Tas 27## # A tibble: 6 x 2
## # Groups: State [6]
## State Articulated_truck_Involved
## <chr> <int>
## 1 ACT 6
## 2 NSW 344
## 3 NT 15
## 4 Qld 278
## 5 SA 104
## 6 Tas 29## # A tibble: 6 x 3
## # Groups: State [2]
## State Type Count
## <chr> <chr> <int>
## 1 ACT Bus_Involved 4
## 2 ACT Heavytruck_Involved 3
## 3 ACT Articulated_truck_Involved 6
## 4 NSW Bus_Involved 75
## 5 NSW Heavytruck_Involved 266
## 6 NSW Articulated_truck_Involved 3443.3.1 Analysis
In this table, we have got the count of different car type of fatal accidents happened group by different states. This data is in long format which will be easier for us to plot the bar chart for better visualization.
3.3.2 Analysis
From the graph above:
- NSW is the state with highest number of fatal accidents
- Follow by Qld and Vic.
In all the states:
- More than half of the total fatal accidents with articulated truck involved.
- Accidents with Heavy Truck involved account for the second large portion of the total accidents
- Follow by bus involved accidents.
Interpretation:
Articulated truck is the heaviest among these 3 type of cars. Heavier car impose a greater impact on any accident which cause a higher fatalities.
Articulated truck is a vehicle which has a permanent or semi-permanent pivot joint in its construction. Only large vehicles need this structure which is easier for them to turn. But it also impose an additional risk for higher fatalities in an accident due to the complex mechanical structure.
Then, we group the data by the crash type and count the number of it.
## # A tibble: 6 x 3
## # Groups: State, Crash.Type [6]
## State Crash.Type Count
## <chr> <chr> <int>
## 1 ACT Multiple 57
## 2 ACT Single 49
## 3 NSW Multiple 1783
## 4 NSW Single 2112
## 5 NT Multiple 129
## 6 NT Single 333
3.3.3 Analysis
From the graph above:
In all the states except ACT and TAS:
- All of the states have more single car crash than multiple car crash in terms of fatal car crashes.
How does the car accident fatality link with the district?
## # A tibble: 6 x 3
## # Groups: State, National.LGA.Name.2017 [6]
## State National.LGA.Name.2017 n
## <chr> <chr> <int>
## 1 Qld <NA> 1786
## 2 NSW <NA> 1778
## 3 Vic <NA> 1638
## 4 WA <NA> 954
## 5 SA <NA> 415
## 6 NT <NA> 220## # A tibble: 6 x 3
## # Groups: State, National.LGA.Name.2017 [6]
## State National.LGA.Name.2017 n
## <chr> <chr> <int>
## 1 Qld Brisbane (C) 87
## 2 NSW Central Coast 82
## 3 Qld Gold Coast (C) 68
## 4 Qld Moreton Bay (R) 62
## 5 NSW Lake Macquarie 56
## 6 NSW Shoalhaven 553.3.4 Analysis
From the table above:
- First table contain value with NA
- In this table, we don’t have detail district information for some cases.
- However, in general, we could see that QLD, NSW, and Vic, these 3 states are top 3 in terms of number of fatal accidents.
- Be aware, this count do not reflect the actual ranking for the number of accidents.
- Second table contain value without NA
- In this table, we can conclude:
- Brisbane in Qld is the district which has the most fatal car accidents happened, which has 87 cases within this 10 years.
- Follow by Central Coast in NSW which is 82 fatal car accidents happened
- The third one is Gold Coast which has 68 cases in this 10 years.
Interpretation:
- QLD, NSW, and Vic are the states that contain top 3 largest amount of population. Therefore, there will be higher chance of having a car accidents.
- It is the same as the district. Brisbane, Central Coast and Gold Coast are the districts with higher population which may leads to higher chance of an accident happened.
3.4 Is there any correlation between accidents and holiday? And why are some explanations for the relation(s) or lack thereof.
## [1] 12 4 1 33.4.1 Analysis
- The month shows that the Christmas period is in December and January. And the Easter period is in March and April.
Figure 3.4: The fatal crashes happen in Christmas period from 2018 to 2020
3.4.2 Analysis
It can be seen from 3.4 that :
The number of crashes increases in VIC, WA and NSW in 2018 and 2019 respectively during Christmas period.
The OLD got a sudden decline in Christmas period of 2020, maybe due to the effectof COVID virus.
For other states, the crashes either fluctuated or drop. Therefore, there is no dominate correlation between crashes and Christmas.
Figure 3.5: The crashes in Easter period from 2018 to 2020
3.4.3 Analysis
In Figure 3.5, in 2018, only WA has a slightly increase in Easter period. For Victoria, the number of crashes drops dramatically in March to April of 2018 and 2020. While other states fluctuate in these period. There is no clear correlation between cashes in Easter period in different states.
Figure 3.6: Fatal crashes happens by day and night
3.4.4 Analysis
The Figure 3.6 compares the fatal crashes that happen in days and night. + Obviously, there are more crashes happen on the days than night.
In 2018 and 2013, the crashes of ACT happen more on night rather than day.
In NT, some crashes happens more at night than day in 2020, 2018, 2015.
3.5 Q. Has accident rate decreased or increased within the past decade (2010- 2020)?
3.5.1 Analysis
Although 2019 was the year the pandemic started, it surprisingly was not the year with the lowest number of fatal crashes in total.
3.6 Q. Possible correlations between number of fatalities and speed limit zone
| Year | <= 40 | 50 | 60 | 70-90 | >= 100 |
|---|---|---|---|---|---|
| 2010 | 7 | 116 | 236 | 283 | 588 |
| 2011 | 18 | 147 | 181 | 239 | 554 |
| 2012 | 21 | 132 | 248 | 277 | 499 |
| 2013 | 16 | 131 | 202 | 251 | 492 |
| 2014 | 18 | 111 | 192 | 229 | 490 |
| 2015 | 20 | 113 | 222 | 249 | 487 |
| 2016 | 17 | 132 | 215 | 290 | 536 |
| 2017 | 35 | 152 | 202 | 241 | 487 |
| 2018 | 18 | 131 | 192 | 228 | 477 |
| 2019 | 21 | 129 | 185 | 241 | 515 |
| 2020 | 18 | 126 | 171 | 234 | 452 |
3.6.1 Analysis
The speed limits are split into 5 respective parts which is 40 and below, 50, 60, 70-90 and 100 and above, all measured in kilometer per hour (km/h). Areas which have less than 40 km/h are often shared zones, school zones and places with high density of pedestrian. 50 km/h are default speed limit within built up areas in every state in Australia except for Northern Territory. 60 km/h are sub-arterial roads, as well as the default speed within built up area in Northern Territory. 70-90 are connector and small highways. 100 and above area highways speed limits.
Between 2010 and 2020, table 3.1 showed that the overall trend in fatal crashes is reduced for speed limit zones outside built-up areas but increased for zones within built-up areas. However, within the period of 2015-2016, all zones see an increase in number of fatal crashes. The number of fatal crashes reduced or increased in varying number as well as rate across the speed zones. With the speed zones of 100 and above and 60 km/h decreased by largest proportion, about 25%.
Figure 3.7: Comparing proportion of yearly fatal crash by speed limit
3.6.2 Analysis
One notable point as figure 3.7 showed is throughout the period analyzed, not only is the speed zone of 100 km/h and above has highest percentage of fatal crash, but it accounts for nearly 50% of all fatal crashes in Australia. While 50 km/h and below speed zones account for less than 10% of all fatal crashes. It is generally well known that the higher the speed a vehicle is traveling at, the longer the stopping distance is, and thus fatal crash is more likely to occur on highways. Kloeden, Woolley & McLean (2007) found that following the reduction of urban speed limit from 60 to 50 km/h in South Australia in 2003, there was a 23% reduction in fatal crashes in 50 km/h zones and 16% reduction in 60 km/h zones.
Figure 3.8: Comparing number of fatalities and fatal crashes by speed zone
Figure 3.9: Percentage difference between fatalities and fatal crashes
3.6.3 Analysis
Figure 3.8 illustrated that not only the number of fatal crashes increased with speed limits, but number of fatalities increased as well, and in higher proportion than the increase of crashes. The difference between number of fatalities and number of crashes are made clearer in figure 3.9. The percentage difference is similar in speed zones of 60 km/h and below, at around 3%. However, the difference for 70-90 km/h is approx. 8% and for highway speed is nearly 13%.
3.6.4 Citations
Kloeden, C., Woolley, J., & McLean, A. J. (2007, October). A follow-up evaluation of the 50km/h default urban speed limit in South Australia. In Proceedings of.